In radiotherapy procedures, a patient usually needs to take a series of 3D CT images for planning and treatment at different stages of disease. To generate a new plan, a physician often needs to contour an image from scratch, which can be time consuming and also lacks consistency with previous contours/plans. Moreover, during treatment, the shapes and locations of contoured organs are usually different from those in the planning image due to changing organ condition during treatment. Directly adopting the plan does not always produce precise dose delivery for optimal treatment. Therefore, it is crucial to have an automatic contouring method to adaptively adjust the contours of the new scans on the fly. It would not only reduce contouring time for the physicians, but also improve the accuracy and consistency for treatment delivery.
Among all the organs under cancer treatment, prostate is a very important one in male pelvic region but very difficult for auto-contouring. Main challenges include: (1) low contrast in 3D CT images, which makes a large portion of the prostate boundary nearly invisible (see FIG. 1); (2) image artifacts produced by prostate seeds (see FIGS. 1(a) and (c)); (3) large area of gas/feces/coil filled in the rectum (see FIG. 1(b), (d), and (e)); and (4) unpredictable prostate condition in different treatment stages.
Currently when making a new plan or during treatment, physicians usually do not utilize the same patient's previous plans or incorporate a patient's previous plans to make a new plan/treatment. Even when previous plans are used, previous plans are incorporated by registration to map the previous contours to the current image. One common method is rigid registration, this method, however, only provides a few degrees of freedom. Thus, the registered contour may not be precise. Deformable registration may be employed to improve the accuracy by calculating non-linear organ deformations. In general, the accuracy of the contour may depend on the number of reference images (atlases) used. There is, however, an increased computational cost in proportion to the number of reference images (atlases) used, which makes it difficult to use for online adaptive planning.
Therefore, it is desirable to develop a new method and system to perform auto-contouring in medical images accurately and efficiently.